2020
DOI: 10.1088/1361-6501/aba3f3
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A parameter-adaptive variational mode decomposition approach based on weighted fuzzy-distribution entropy for noise source separation

Abstract: Due to limitations in the generalisation ability of currently proposed improved variational mode decomposition (VMD) methods, it is hard to precisely and efficiently discern signal characteristics from different power systems. Meanwhile, it is difficult to separate non-order noise sources in current studies. To address this issue, a novel scheme is proposed based on parameter-adaptive VMD and partial coherence analysis (PCA) for separating noise sources. In this approach, weighted fuzzy-distribution entropy (F… Show more

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Cited by 11 publications
(5 citation statements)
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“…In general, the fault signal of rolling bearings contains three components, namely the transient impulse train d (caused by rolling bearing faults), environmental noise e, and discrete harmonic signal u [30,31]. These three components are affected by system dynamic transmission-path effect h.…”
Section: Deconvolution Problemmentioning
confidence: 99%
“…In general, the fault signal of rolling bearings contains three components, namely the transient impulse train d (caused by rolling bearing faults), environmental noise e, and discrete harmonic signal u [30,31]. These three components are affected by system dynamic transmission-path effect h.…”
Section: Deconvolution Problemmentioning
confidence: 99%
“…In this regard, scholars have adopted the idea of entropy to optimize the parameters of VMD adaptively. Guo et al 18 used the multi-scale permutation entropy (MPE) threshold method to select the appropriate VMD parameters, and obtained the optimal intrinsic mode function (IMF) components of the bearing fault signal; He et al 19 used the improved sparrow search algorithm to optimize VMD parameters with the dispersion entropy as the fitness value, and effective noise reduction of bearing fault signals was achieved; Ren et al 20 used the improved adaptive genetic algorithm (IAGA) to optimize VMD parameters with the permutation entropy as the fitness value, thus realizing the separation of effective information of diesel engine fault signals; considering the complexity of the signal system and the mutual information between the decomposed components and the original signal, Zhou et al 21 constructed a weighted fuzzy distribution entropy (FuzzDistEn) to optimize VMD to adaptively obtain optimal parameters; this method successfully separates five noise sources in the diesel engine vibration signal and improves the SNR of the signal. Inspired by the above research, to realize adaptive noise reduction for different types of fault signals of bearings and diesel engines and retain rich fault information, this paper uses the gray wolf optimization algorithm (GWO) to optimize the two important parameters of Alpha and K in VMD with the power spectrum entropy as the fitness value.…”
Section: Introductionmentioning
confidence: 99%
“…Dibaj et al [22] determined the optimal values of decomposition parameters K and alpha by judging the adaptive indices, including the mean correlation coefficients between the adjacent modes and the energy loss coefficient between the original signal and the reconstructed signal, which should not exceed the defined thresholds for optimal values. With the rapid development of bionic optimization technology, various metaheuristic algorithms were used to simultaneously optimize the K and α of VMD, such as a genetic algorithm (GA) [23], particle swarm optimization (PSO) [24], a grasshopper optimization algorithm (GOA) [25], and a whale optimization algorithm (WOA) [26]. Although these parameter-adaptive VMD methods have achieved good results in their respective research fields, the inefficiency of the optimization process is an urgent problem.…”
Section: Introductionmentioning
confidence: 99%